
"""simple docstring"""


import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


SCREAMING_SNAKE_CASE			  =  logging.getLogger(__name__)



@dataclass
class 	__a	:
       UpperCamelCase_		:       str				=      field(
           metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}						)
       UpperCamelCase_		:       Optional[str]				=      field(
           default=snake_case_     ,  metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}						)
       UpperCamelCase_		:       Optional[str]				=      field(
           default='''NER'''     ,  metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}						)
       UpperCamelCase_		:       Optional[str]				=      field(
           default=snake_case_     ,  metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}						)
       UpperCamelCase_		:       bool				=      field(default=snake_case_     ,  metadata={'''help''': '''Set this flag to use fast tokenization.'''}						)
       # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
       # or just modify its tokenizer_config.json.
       UpperCamelCase_		:       Optional[str]				=      field(
           default=snake_case_     ,  metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}     ,  )





@dataclass
class 	__a	:
       UpperCamelCase_		:       str				=      field(
           metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}						)
       UpperCamelCase_		:       Optional[str]				=      field(
           default=snake_case_     ,  metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}     ,  )
       UpperCamelCase_		:       int				=      field(
           default=128     ,  metadata={
               '''help''': (
                   '''The maximum total input sequence length after tokenization. Sequences longer '''
                   '''than this will be truncated, sequences shorter will be padded.'''
               )
           }     ,  )
       UpperCamelCase_		:       bool				=      field(
           default=snake_case_     ,  metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}						)





def 						lowerCamelCase__      (				)->				str:







       """simple docstring"""
       # See all possible arguments in src/transformers/training_args.py
       # or by passing the --help flag to this script.
       # We now keep distinct sets of args, for a cleaner separation of concerns.

       UpperCamelCase       			=		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
       if len(sys.argv					) == 2 and sys.argv[1].endswith(".json"					):
              # If we pass only one argument to the script and it's the path to a json file,
              # let's parse it to get our arguments.
              UpperCamelCase       			=		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
       else:
              UpperCamelCase       			=		parser.parse_args_into_dataclasses()

       if (
           os.path.exists(training_args.output_dir					)
           and os.listdir(training_args.output_dir					)
           and training_args.do_train
           and not training_args.overwrite_output_dir
       ):
              raise ValueError(
                  F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
                  " --overwrite_output_dir to overcome."					)

       UpperCamelCase       			=		import_module("tasks"					)
       try:
              UpperCamelCase       			=		getattr(UpperCAmelCase_		,   model_args.task_type					)
              UpperCamelCase       			=		token_classification_task_clazz()
       except AttributeError:
              raise ValueError(
                  F"Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
                  F"Available tasks classes are: {TokenClassificationTask.__subclasses__()}"					)

       # Setup logging
       logging.basicConfig(
           format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"		,   datefmt="%m/%d/%Y %H:%M:%S"		,   level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN		,   )
       logger.warning(
           "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"		,   training_args.local_rank		,   training_args.device		,   training_args.n_gpu		,   bool(training_args.local_rank != -1					)		,   training_args.fpaa		,   )
       # Set the verbosity to info of the Transformers logger (on main process only):
       if is_main_process(training_args.local_rank					):
              transformers.utils.logging.set_verbosity_info()
              transformers.utils.logging.enable_default_handler()
              transformers.utils.logging.enable_explicit_format()
       logger.info("Training/evaluation parameters %s"		,   UpperCAmelCase_					)

       # Set seed
       set_seed(training_args.seed					)

       # Prepare CONLL-2003 task
       UpperCamelCase       			=		token_classification_task.get_labels(data_args.labels					)
       UpperCamelCase       			=		dict(enumerate(UpperCAmelCase_					)					)
       UpperCamelCase       			=		len(UpperCAmelCase_					)

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.

       UpperCamelCase       			=		AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path		,   num_labels=UpperCAmelCase_		,   idalabel=UpperCAmelCase_		,   labelaid={label: i for i, label in enumerate(UpperCAmelCase_					)}		,   cache_dir=model_args.cache_dir		,   )
       UpperCamelCase       			=		AutoTokenizer.from_pretrained(
           model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path		,   cache_dir=model_args.cache_dir		,   use_fast=model_args.use_fast		,   )
       UpperCamelCase       			=		AutoModelForTokenClassification.from_pretrained(
           model_args.model_name_or_path		,   from_tf=bool(".ckpt" in model_args.model_name_or_path					)		,   config=UpperCAmelCase_		,   cache_dir=model_args.cache_dir		,   )

       # Get datasets
       UpperCamelCase       			=		(
           TokenClassificationDataset(
               token_classification_task=UpperCAmelCase_		,   data_dir=data_args.data_dir		,   tokenizer=UpperCAmelCase_		,   labels=UpperCAmelCase_		,   model_type=config.model_type		,   max_seq_length=data_args.max_seq_length		,   overwrite_cache=data_args.overwrite_cache		,   mode=Split.train		,   )
           if training_args.do_train
           else None
       )
       UpperCamelCase       			=		(
           TokenClassificationDataset(
               token_classification_task=UpperCAmelCase_		,   data_dir=data_args.data_dir		,   tokenizer=UpperCAmelCase_		,   labels=UpperCAmelCase_		,   model_type=config.model_type		,   max_seq_length=data_args.max_seq_length		,   overwrite_cache=data_args.overwrite_cache		,   mode=Split.dev		,   )
           if training_args.do_eval
           else None
       )

       def align_predictions(UpperCAmelCase_		,   UpperCAmelCase_					) -> Tuple[List[int], List[int]]:
              UpperCamelCase       			=		np.argmax(UpperCAmelCase_		,   axis=2					)

              UpperCamelCase       			=		preds.shape

              UpperCamelCase       			=		[[] for _ in range(UpperCAmelCase_					)]
              UpperCamelCase       			=		[[] for _ in range(UpperCAmelCase_					)]

              for i in range(UpperCAmelCase_					):
                     for j in range(UpperCAmelCase_					):
                            if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
                                   out_label_list[i].append(label_map[label_ids[i][j]]					)
                                   preds_list[i].append(label_map[preds[i][j]]					)

              return preds_list, out_label_list

       def compute_metrics(UpperCAmelCase_					) -> Dict:
              UpperCamelCase       			=		align_predictions(p.predictions		,   p.label_ids					)
              return {
                  "accuracy_score": accuracy_score(UpperCAmelCase_		,   UpperCAmelCase_					),
                  "precision": precision_score(UpperCAmelCase_		,   UpperCAmelCase_					),
                  "recall": recall_score(UpperCAmelCase_		,   UpperCAmelCase_					),
                  "f1": fa_score(UpperCAmelCase_		,   UpperCAmelCase_					),
              }

       # Data collator
       UpperCamelCase       			=		DataCollatorWithPadding(UpperCAmelCase_		,   pad_to_multiple_of=8					) if training_args.fpaa else None

       # Initialize our Trainer
       UpperCamelCase       			=		Trainer(
           model=UpperCAmelCase_		,   args=UpperCAmelCase_		,   train_dataset=UpperCAmelCase_		,   eval_dataset=UpperCAmelCase_		,   compute_metrics=UpperCAmelCase_		,   data_collator=UpperCAmelCase_		,   )

       # Training
       if training_args.do_train:
              trainer.train(
                  model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path					) else None					)
              trainer.save_model()
              # For convenience, we also re-save the tokenizer to the same directory,
              # so that you can share your model easily on huggingface.co/models =)
              if trainer.is_world_process_zero():
                     tokenizer.save_pretrained(training_args.output_dir					)

    # Evaluation
       UpperCamelCase       			=		{}
       if training_args.do_eval:
              logger.info("*** Evaluate ***"					)

              UpperCamelCase       			=		trainer.evaluate()

              UpperCamelCase       			=		os.path.join(training_args.output_dir		,   "eval_results.txt"					)
              if trainer.is_world_process_zero():
                     with open(UpperCAmelCase_		,   "w"					) as writer:
                            logger.info("***** Eval results *****"					)
                            for key, value in result.items():
                                   logger.info("  %s = %s"		,   UpperCAmelCase_		,   UpperCAmelCase_					)
                                   writer.write("%s = %s\n" % (key, value)					)

                     results.update(UpperCAmelCase_					)

    # Predict
       if training_args.do_predict:
              UpperCamelCase       			=		TokenClassificationDataset(
                  token_classification_task=UpperCAmelCase_		,   data_dir=data_args.data_dir		,   tokenizer=UpperCAmelCase_		,   labels=UpperCAmelCase_		,   model_type=config.model_type		,   max_seq_length=data_args.max_seq_length		,   overwrite_cache=data_args.overwrite_cache		,   mode=Split.test		,   )

              UpperCamelCase       			=		trainer.predict(UpperCAmelCase_					)
              UpperCamelCase       			=		align_predictions(UpperCAmelCase_		,   UpperCAmelCase_					)

              UpperCamelCase       			=		os.path.join(training_args.output_dir		,   "test_results.txt"					)
              if trainer.is_world_process_zero():
                     with open(UpperCAmelCase_		,   "w"					) as writer:
                            for key, value in metrics.items():
                                   logger.info("  %s = %s"		,   UpperCAmelCase_		,   UpperCAmelCase_					)
                                   writer.write("%s = %s\n" % (key, value)					)

        # Save predictions
              UpperCamelCase       			=		os.path.join(training_args.output_dir		,   "test_predictions.txt"					)
              if trainer.is_world_process_zero():
                     with open(UpperCAmelCase_		,   "w"					) as writer:
                            with open(os.path.join(data_args.data_dir		,   "test.txt"					)		,   "r"					) as f:
                                   token_classification_task.write_predictions_to_file(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)

       return results



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				str:







       """simple docstring"""
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
      main()










'''simple docstring'''



def        UpperCAmelCase (    A	:					int       ,		A	:					int					):
							return x if y == 0 else greatest_common_divisor(A       ,		x % y					)
def        UpperCAmelCase (    A	:					int       ,		A	:					int					):
							return (x * y) // greatest_common_divisor(A       ,		A					)
def        UpperCAmelCase (    A	:					int = 20					):
							SCREAMING_SNAKE_CASE       :   Tuple         =      1
							for i in range(1       ,		n + 1					):
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      lcm(A       ,		A					)
							return g


if __name__ == "__main__":
							print(f'{solution() = }')




